Targeted sentiment classification aims to identify the sentiment expressed towards some targets given context sentences, having great application value in social media, ecommerce platform and other fields. Most of the previous methods model context and target words with RNN and attention mechanism, which primarily do not use any external knowledge. In this paper, we utilize external knowledge from knowledge bases to reinforce the semantic representation of context and target. We propose a new model called Knowledge Powered Attention Network (KPAN), which uses the multi-head attention mechanism to represent target and context and to fuse with conceptual knowledge extracted from external knowledge bases. The experiments on three public datasets revealed that our proposed model outperforms the state-of-the-art methods, which signify the validity of our model.
{"title":"Targeted Sentiment Classification with Knowledge Powered Attention Network","authors":"Ximo Bian, Chong Feng, Arshad Ahmad, Jinming Dai, Guifen Zhao","doi":"10.1109/ICTAI.2019.00150","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00150","url":null,"abstract":"Targeted sentiment classification aims to identify the sentiment expressed towards some targets given context sentences, having great application value in social media, ecommerce platform and other fields. Most of the previous methods model context and target words with RNN and attention mechanism, which primarily do not use any external knowledge. In this paper, we utilize external knowledge from knowledge bases to reinforce the semantic representation of context and target. We propose a new model called Knowledge Powered Attention Network (KPAN), which uses the multi-head attention mechanism to represent target and context and to fuse with conceptual knowledge extracted from external knowledge bases. The experiments on three public datasets revealed that our proposed model outperforms the state-of-the-art methods, which signify the validity of our model.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130017578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00154
April H. Liu, Zihao Cheng, Justin Jiang
In classification problem, Bayesian networks play an important role because of its efficiency and interpretability. Bayesian networks learning methods require enough data to produce reliable results. Unfortunately, in practice, the training data are often either too few, expensive to label, or easy to be outdated. However, there may be sufficient labeled data that are available in a different but related domain. Learning reliable Bayesian networks from limited data is difficult; and transfer learning might be used to improve the robustness of learned networks by combining data from auxiliary and related labeled dataset. In this paper, we propose a novel transfer learning method for Bayesian networks for classification that considers both structure and parameter learning. Our solution is to first construct the initial Bayesian networks model for auxiliary labeled data, and then revise the model according to an Expectation-Maximization (EM) algorithm, structure and parameters are revised by turns, in order to make it applicable to the target unlabeled dataset. We mainly apply our method on a special type of Bayesian networks, namely tree-based Bayesian network. To validate our approach, we evaluated the method on a real and typical classification scenario - text classification problem. We compared our method with other transfer learning method as well as the traditional supervised and semi-supervised learning algorithms. The experimental results show that our algorithm is very effective and obtains a significant improvement when we transfer knowledge from related dataset.
{"title":"Bayesian Network Learning for Classification via Transfer Method","authors":"April H. Liu, Zihao Cheng, Justin Jiang","doi":"10.1109/ICTAI.2019.00154","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00154","url":null,"abstract":"In classification problem, Bayesian networks play an important role because of its efficiency and interpretability. Bayesian networks learning methods require enough data to produce reliable results. Unfortunately, in practice, the training data are often either too few, expensive to label, or easy to be outdated. However, there may be sufficient labeled data that are available in a different but related domain. Learning reliable Bayesian networks from limited data is difficult; and transfer learning might be used to improve the robustness of learned networks by combining data from auxiliary and related labeled dataset. In this paper, we propose a novel transfer learning method for Bayesian networks for classification that considers both structure and parameter learning. Our solution is to first construct the initial Bayesian networks model for auxiliary labeled data, and then revise the model according to an Expectation-Maximization (EM) algorithm, structure and parameters are revised by turns, in order to make it applicable to the target unlabeled dataset. We mainly apply our method on a special type of Bayesian networks, namely tree-based Bayesian network. To validate our approach, we evaluated the method on a real and typical classification scenario - text classification problem. We compared our method with other transfer learning method as well as the traditional supervised and semi-supervised learning algorithms. The experimental results show that our algorithm is very effective and obtains a significant improvement when we transfer knowledge from related dataset.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131758919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00039
A. Azaria, Seagal Azaria
Despite being a well-researched problem, ovulation detection in human female remains a difficult task. Most current methods for ovulation detection rely on measurements of a single property (e.g. morning body temperature) or at most on two properties (e.g. both salivary and vaginal electrical resistance). In this paper we present a machine learning based method for detecting the day in which ovulation occurs. Our method considered measurements of five different properties. We crawled a data-set from the web and showed that our method outperforms current state-of-the-art methods for ovulation detection. Our method performs well also when considering measurements of fewer properties. We show that our method's performance can be further improved by using unlabeled data, that is, mensuration cycles without a know ovulation date. Our resulted machine learning model can be very useful for women trying to conceive that have trouble in recognizing their ovulation period, especially when some measurements are missing.
{"title":"Semi-Supervised Ovulation Detection Based on Multiple Properties","authors":"A. Azaria, Seagal Azaria","doi":"10.1109/ICTAI.2019.00039","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00039","url":null,"abstract":"Despite being a well-researched problem, ovulation detection in human female remains a difficult task. Most current methods for ovulation detection rely on measurements of a single property (e.g. morning body temperature) or at most on two properties (e.g. both salivary and vaginal electrical resistance). In this paper we present a machine learning based method for detecting the day in which ovulation occurs. Our method considered measurements of five different properties. We crawled a data-set from the web and showed that our method outperforms current state-of-the-art methods for ovulation detection. Our method performs well also when considering measurements of fewer properties. We show that our method's performance can be further improved by using unlabeled data, that is, mensuration cycles without a know ovulation date. Our resulted machine learning model can be very useful for women trying to conceive that have trouble in recognizing their ovulation period, especially when some measurements are missing.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133085334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00136
Lucas O. Souza, G. Ramos, C. Ralha
The idea of experience sharing between cooperative agents naturally emerges from our understanding of how humans learn. Our evolution as a species is tightly linked to the ability to exchange learned knowledge with one another. It follows that experience sharing (ES) between autonomous and independent agents could become the key to accelerate learning in cooperative multiagent settings. We investigate if randomly selecting experiences to share can increase the performance of deep reinforcement learning agents, and propose three new methods for selecting experiences to accelerate the learning process. Firstly, we introduce Focused ES, which prioritizes unexplored regions of the state space. Secondly, we present Prioritized ES, in which temporal-difference error is used as a measure of priority. Finally, we devise Focused Prioritized ES, which combines both previous approaches. The methods are empirically validated in a control problem. While sharing randomly selected experiences between two Deep Q-Network agents shows no improvement over a single agent baseline, we show that the proposed ES methods can successfully outperform the baseline. In particular, the Focused ES accelerates learning by a factor of 2, reducing by 51% the number of episodes required to complete the task.
{"title":"Experience Sharing Between Cooperative Reinforcement Learning Agents","authors":"Lucas O. Souza, G. Ramos, C. Ralha","doi":"10.1109/ICTAI.2019.00136","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00136","url":null,"abstract":"The idea of experience sharing between cooperative agents naturally emerges from our understanding of how humans learn. Our evolution as a species is tightly linked to the ability to exchange learned knowledge with one another. It follows that experience sharing (ES) between autonomous and independent agents could become the key to accelerate learning in cooperative multiagent settings. We investigate if randomly selecting experiences to share can increase the performance of deep reinforcement learning agents, and propose three new methods for selecting experiences to accelerate the learning process. Firstly, we introduce Focused ES, which prioritizes unexplored regions of the state space. Secondly, we present Prioritized ES, in which temporal-difference error is used as a measure of priority. Finally, we devise Focused Prioritized ES, which combines both previous approaches. The methods are empirically validated in a control problem. While sharing randomly selected experiences between two Deep Q-Network agents shows no improvement over a single agent baseline, we show that the proposed ES methods can successfully outperform the baseline. In particular, the Focused ES accelerates learning by a factor of 2, reducing by 51% the number of episodes required to complete the task.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133043566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Knowledge graph completion can predict the possible relation between entities. Previous work such as TransE, TransR, TransPES and GTrans embed knowledge graph into vector space and treat relations between entities as translations. In most cases, the more complex the algorithm is, the better the result will be, but it is difficult to apply to large-scale knowledge graphs. Therefore, we propose TransB, an efficient model, in this paper. We avoid the complex matrix or vector multiplication operation. Meanwhile, we make the representation of entities not too simple, which can satisfy the operation in the case of non-one-to-one relation. We use link prediction to evaluate the performance of our model in the experiment. The experimental results show that our model is valid and has low time complexity.
{"title":"Knowledge Graph Embedding by Bias Vectors","authors":"Minjie Ding, W. Tong, Xuehai Ding, Xiaoli Zhi, Xiao Wang, Guoqing Zhang","doi":"10.1109/ICTAI.2019.00180","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00180","url":null,"abstract":"Knowledge graph completion can predict the possible relation between entities. Previous work such as TransE, TransR, TransPES and GTrans embed knowledge graph into vector space and treat relations between entities as translations. In most cases, the more complex the algorithm is, the better the result will be, but it is difficult to apply to large-scale knowledge graphs. Therefore, we propose TransB, an efficient model, in this paper. We avoid the complex matrix or vector multiplication operation. Meanwhile, we make the representation of entities not too simple, which can satisfy the operation in the case of non-one-to-one relation. We use link prediction to evaluate the performance of our model in the experiment. The experimental results show that our model is valid and has low time complexity.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133600239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00132
Isuru Wijesinghe, C. Gamage, Charith D. Chitraranjan
The automatic classification of diabetic retinopathy (DR) is of vital importance, as it is the leading cause of irreversible vision loss in the working-age population all over the world today. Current clinical approaches require a well-trained clinician to manually evaluate digital colour fundus photographs of retina and locate lesions associated with vascular abnormalities due to diabetes, which is time-consuming. Recently, deep feature extraction using pretrained convolutional neural networks has been used to predict DR from fundus images with reasonable accuracy. However, techniques such as global average pooling (GAP), singular value decomposition (SVD) and ensemble learning have not been used in automatic prediction of DR. We propose to use a combination of deep features produced by an ensemble of pretrained-CNNs (DenseNet-201, ResNet-18 and VGG-16) as a single feature vector to predict five-class severity levels of diabetic retinopathy. Our results show a promising F1-measure of over 98% on the kaggle dataset and another dataset provided to us by an ophthalmic clinic. This is an improvement on the current state-of-the-art approaches in DR classification. We evaluated prominent CNN architectures (DenseNet, ResNet, Xception, InceptionV3, InceptionResNetV2 and VGG) that can be used for the task of transfer learning for DR. Moreover, we describe a technique of reducing memory consumption and processing time whereas preserving classification accuracy by using dimensional reduction based on GAP and SVD.
{"title":"Transfer Learning with Ensemble Feature Extraction and Low-Rank Matrix Factorization for Severity Stage Classification of Diabetic Retinopathy","authors":"Isuru Wijesinghe, C. Gamage, Charith D. Chitraranjan","doi":"10.1109/ICTAI.2019.00132","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00132","url":null,"abstract":"The automatic classification of diabetic retinopathy (DR) is of vital importance, as it is the leading cause of irreversible vision loss in the working-age population all over the world today. Current clinical approaches require a well-trained clinician to manually evaluate digital colour fundus photographs of retina and locate lesions associated with vascular abnormalities due to diabetes, which is time-consuming. Recently, deep feature extraction using pretrained convolutional neural networks has been used to predict DR from fundus images with reasonable accuracy. However, techniques such as global average pooling (GAP), singular value decomposition (SVD) and ensemble learning have not been used in automatic prediction of DR. We propose to use a combination of deep features produced by an ensemble of pretrained-CNNs (DenseNet-201, ResNet-18 and VGG-16) as a single feature vector to predict five-class severity levels of diabetic retinopathy. Our results show a promising F1-measure of over 98% on the kaggle dataset and another dataset provided to us by an ophthalmic clinic. This is an improvement on the current state-of-the-art approaches in DR classification. We evaluated prominent CNN architectures (DenseNet, ResNet, Xception, InceptionV3, InceptionResNetV2 and VGG) that can be used for the task of transfer learning for DR. Moreover, we describe a technique of reducing memory consumption and processing time whereas preserving classification accuracy by using dimensional reduction based on GAP and SVD.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128863579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00011
Yazid Boumarafi, Y. Salhi
In this paper, we aim at studying the Exactly-One-SAT problem (in short EO-SAT). This problem consists in deciding whether a given CNF formula admits a model so that each clause has exactly one satisfied literal. The contribution of this work is twofold. Firstly, we introduce a tractable class in EO-SAT, which is defined by a property that has to be satisfied by combinations of clauses. This class can be seen as a counterpart of tractable classes in the maximum independent set problem. Secondly, we propose graph-based approaches for reducing the number of variables and clauses of EO-SAT instances, which consequently allow for reducing the search space. We provide an experimental study for evaluating these approach by showing its interest in the context of the graph coloring problem.
{"title":"On Solving Exactly-One-SAT","authors":"Yazid Boumarafi, Y. Salhi","doi":"10.1109/ICTAI.2019.00011","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00011","url":null,"abstract":"In this paper, we aim at studying the Exactly-One-SAT problem (in short EO-SAT). This problem consists in deciding whether a given CNF formula admits a model so that each clause has exactly one satisfied literal. The contribution of this work is twofold. Firstly, we introduce a tractable class in EO-SAT, which is defined by a property that has to be satisfied by combinations of clauses. This class can be seen as a counterpart of tractable classes in the maximum independent set problem. Secondly, we propose graph-based approaches for reducing the number of variables and clauses of EO-SAT instances, which consequently allow for reducing the search space. We provide an experimental study for evaluating these approach by showing its interest in the context of the graph coloring problem.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115439483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00-94
X. Zhong, Jiachen Li, Wenxin Huang, Liang Xie
Due to its low storage cost and fast query speed, hashing has been widely applied to approximate nearest neighbor search for large-scale image retrieval, while deep hashing further improves the retrieval quality by learning a good image representation. However, existing deep hash methods simplify multi-label images into single-label processing, so the rich semantic information from multi-label is ignored. Meanwhile, the imbalance of similarity information leads to the wrong sample weight in the loss function, which makes unsatisfactory training performance and lower recall rate. In this paper, we propose Deep Multi-Label Hashing (DMLH) model that generates binary hash codes which retain the semantic relationship of multi-label of the image. The contributions of this new model mainly include the following two aspects: (1) A novel sample weight calculation model adaptively adjusts the weight of the sample pair by calculating the semantic similarity of the multi-label image pairs. (2) The sample weight cross-entropy loss function, which is designed according to the similarity of the image, adjusts the balance of similar image pairs and dissimilar image pairs. Extensive experiments demonstrate that the proposed method can generate hash codes which achieve better retrieval performance on two benchmark datasets, NUS-WIDE and MS-COCO.
{"title":"Deep Multi-label Hashing for Image Retrieval","authors":"X. Zhong, Jiachen Li, Wenxin Huang, Liang Xie","doi":"10.1109/ICTAI.2019.00-94","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00-94","url":null,"abstract":"Due to its low storage cost and fast query speed, hashing has been widely applied to approximate nearest neighbor search for large-scale image retrieval, while deep hashing further improves the retrieval quality by learning a good image representation. However, existing deep hash methods simplify multi-label images into single-label processing, so the rich semantic information from multi-label is ignored. Meanwhile, the imbalance of similarity information leads to the wrong sample weight in the loss function, which makes unsatisfactory training performance and lower recall rate. In this paper, we propose Deep Multi-Label Hashing (DMLH) model that generates binary hash codes which retain the semantic relationship of multi-label of the image. The contributions of this new model mainly include the following two aspects: (1) A novel sample weight calculation model adaptively adjusts the weight of the sample pair by calculating the semantic similarity of the multi-label image pairs. (2) The sample weight cross-entropy loss function, which is designed according to the similarity of the image, adjusts the balance of similar image pairs and dissimilar image pairs. Extensive experiments demonstrate that the proposed method can generate hash codes which achieve better retrieval performance on two benchmark datasets, NUS-WIDE and MS-COCO.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114923490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00153
Himanshu Singhal, Harish Ravi, S. N. Chakravarthy, Prabavathy Balasundaram, Chitra Babu
The healthcare industry, through digitization, is trying to achieve interoperability, but has not been able to achieve complete Health Information Exchange (HIE). One of the major challenges in achieving this is the inability to accurately match patient data. Mismatching of patient records can lead to improper treatment which can prove to be fatal. Also, the presence of duplicate overheads has caused inaccessibility to crucial information in the time of need. Existing solutions to patient matching are both time-consuming and non-scalable. This paper proposes a framework, namely, Electronic Patient Matching System (EPMS), which attempts to overcome these barriers while achieving a good accuracy in matching patient records. The framework encodes the patient records using variational autoencoder and amalgamates them by performing locality sensitive hashing on an Apache spark cluster. This makes the process faster and highly scalable. Furthermore, a fuzzy matching of the records in each block is performed using Levenshtein distances to identify the duplicate patient records. Experimental investigations were performed on a synthetically generated dataset consisting of 44555 patient records. The proposed framework achieved a matching accuracy of 81.15% on this dataset.
{"title":"EPMS: A Framework for Large-Scale Patient Matching","authors":"Himanshu Singhal, Harish Ravi, S. N. Chakravarthy, Prabavathy Balasundaram, Chitra Babu","doi":"10.1109/ICTAI.2019.00153","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00153","url":null,"abstract":"The healthcare industry, through digitization, is trying to achieve interoperability, but has not been able to achieve complete Health Information Exchange (HIE). One of the major challenges in achieving this is the inability to accurately match patient data. Mismatching of patient records can lead to improper treatment which can prove to be fatal. Also, the presence of duplicate overheads has caused inaccessibility to crucial information in the time of need. Existing solutions to patient matching are both time-consuming and non-scalable. This paper proposes a framework, namely, Electronic Patient Matching System (EPMS), which attempts to overcome these barriers while achieving a good accuracy in matching patient records. The framework encodes the patient records using variational autoencoder and amalgamates them by performing locality sensitive hashing on an Apache spark cluster. This makes the process faster and highly scalable. Furthermore, a fuzzy matching of the records in each block is performed using Levenshtein distances to identify the duplicate patient records. Experimental investigations were performed on a synthetically generated dataset consisting of 44555 patient records. The proposed framework achieved a matching accuracy of 81.15% on this dataset.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"273 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122119550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00144
Spyridon Manganas, N. Bourbakis
EEG has been extensively used to aid the diagnosis of various brain disorders and also, for the identification of brain activities during cognitive tasks. However, the visual evaluation of EEG recordings is a demanding process, susceptible to error and bias due to the human factor involved. The development of EEG analysis methods coupled with data processing and mining techniques have assisted the feature extraction process from EEG recordings. In this paper, a novel method for classification of EEG signals based on features derived from the EEG morphology is proposed. The classification accuracy, as illustrated through experiment evaluation, shows that the proposed method can achieve adequate results and moreover the extracted features can be used collaboratively with commonly used features from time and time-frequency domain to increase the EEG signal's classification performance.
{"title":"A Novel Learning Classification Scheme for Brain EEG Patterns","authors":"Spyridon Manganas, N. Bourbakis","doi":"10.1109/ICTAI.2019.00144","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00144","url":null,"abstract":"EEG has been extensively used to aid the diagnosis of various brain disorders and also, for the identification of brain activities during cognitive tasks. However, the visual evaluation of EEG recordings is a demanding process, susceptible to error and bias due to the human factor involved. The development of EEG analysis methods coupled with data processing and mining techniques have assisted the feature extraction process from EEG recordings. In this paper, a novel method for classification of EEG signals based on features derived from the EEG morphology is proposed. The classification accuracy, as illustrated through experiment evaluation, shows that the proposed method can achieve adequate results and moreover the extracted features can be used collaboratively with commonly used features from time and time-frequency domain to increase the EEG signal's classification performance.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125830035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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